Quantitative methods for the assessment of systematic error in observational studies: improving causal research

流行病学研究的结果往往看起来相互矛盾，导致普通公众对医学研究持怀疑态度。如果评估和解释影响这类研究的不确定性的真实程度，其中许多矛盾是可以避免的。目前，研究有限样本产生的不确定度通常是唯一存在的不确定度(以可信区间表示)。然而，其他不确定性来源可能使这种不确定性相形见绌：(1)研究人群中的重要未测量因素(未测量的混淆)；(2)受试者的非随机纳入(选择偏差)；(3)暴露和结果的测量误差(测量误差)；(4)某些受试者缺乏结果/暴露数据(缺失数据)。最近，考虑到这些不确定性来源的新方法被提出，但面临着操作(例如如何确定可能的选择偏差的大小)和方法(例如如何将这些信息与观察到的数据结合在一起)的困难。我们将研究三种改进不确定性量化的替代方法：经典灵敏度分析、蒙特卡罗灵敏度分析和贝叶斯偏差分析。我们将探讨这些方法在不同情况下的可行性，并就稳健、透明和可供广泛从业者使用的方法向从业者提供实用指南(包括用户友好的软件)。

Results from epidemiological studies often appear contradictory, leading to some cynicism regarding medical research among the general public. Many of these contradictions might be avoided if the true extent of the uncertainties that affect such research was assessed and explained. Presently, uncertainty arising from studying finite samples is typically the only uncertainty presented (as confidence intervals). However, other sources of uncertainty may dwarf this uncertainty: (i) important unmeasured factors in the study population (unmeasured confounding); (ii) non-random inclusion of subjects (selection bias); (iii) errors in measurements of exposure and outcome (measurement error) (iv) absence of outcome/exposure data for some subjects (missing data). Recently new approaches, which take account of these sources of uncertainty, have been proposed, but face both operational (e.g. how to specify the likely magnitude of selection bias) and methodological (e.g. how to combine this information with the observed data) difficulties. We shall investigate three alternative approaches to improved quantification of uncertainty; classical sensitivity analysis; Monte Carlo sensitivity analysis and Bayesian bias analysis. We will explore the feasibility of these approaches in different contexts and provide practical guidelines to practitioners (including user-friendly software) on methods which are robust, transparent and accessible to a wide range of practitioners.